Month: June 2010

Consider a credit portfolio that consists of default-sensitive instru¬ments such as lines of credit, corporate bonds, and government bonds. The corresponding credit value-at-risk (VaR), is the minimum loss of next year if the worst 0.03 percent event happens. In another words, 99.97 percent of the time the loss will not be greater than VaR. Note that the credit VaR is measured at the time span of one year and is different from the 10-day convention adopted by market VaR. 0.03 percent is chosen because it is a rating agency standard of granting an AA credit rating.

Single instrument
The loss of a single instrument can be decomposed into three components: the default probability of the obligor (PD), the loss given default (LGD), and the exposure at default (EAD). For the sake of simplicity, EAD is assumed to be non-random in the subsequent discussion.

LGD is the portion of EAD that gives negative impact in case of default. LGD is usually less than one because many default obligors are originally backed by securities.

The magnitude of the recovery rate is tied to the collateral properties during or after default. The recovery rate depends on the nature of the instrument: only the loss on principal can be claimed, not the loss on coupon interest.
PD and LGD are positively correlated, meaning PD and the recovery rate are negatively correlated.

Portfolio
The main issue in computing VaR for a credit portfolio is that the joint default probability for two obligors does not follow the law of independence. Companies in the same sector tend to default together. This is known as credit concentration.

Stress testing
Stress testing is the procedure of checking the robustness of VaR under different hypothetical changes. Examples include perturbation of the model parameters, economic downturn of the region, deterioration of the industry environment, or the downgrade of specific obligors’ credit profiles. Another equivalent way is to fix VaR and observe how the tail area of L is affected. A systematic account can be found in the Bank of International Settlement document of “Stress testing at major financial institutions: survey results and practice”.

Implementation details
Some hints on the real complexity of VaR:
• Although many banks have a strong desire to apply credit VaR to both trading and loan books in an integrated manner, some of the cumbersome barriers are the differences in accounting treatment, variation of technology platforms, and illiquidity factors (relating to traditional loans). The banks may involve fundamental changes in organisational structure in order to implement consistent integrated risk management systems.

• Some of the companies involved in a portfolio could have become public very recently and the equity return may not be available before IPO. Statistical techniques, such as EM algorithm of Dempster et al. and data augmentation algorithm of Tanner and Wong, can be employed to impute the missing values and estimate the model parameters.

• Similar to market VaR, backtesting is one of the goals to be achieved in addition to stress testing. However, the time span of credit VaR is typically one year and it is hopeless to collect enough historic credit loss data for validation purpose. Lopez and Saidenberg suggest backtesting by cross-sectional simulation, which is essentially a variation of bootstrap, ie, evaluation based upon resampled data.

This article is an edited version of
an entry in the “Encyclopedia of Quantitative Risk Analysis and
Assessment”, Copyright © 2008 John Wiley & Sons Ltd. Used by
permission.

www.wiley.com

Mismanagement of past environmental risk issues, by both scientists and policy makers have left the public distrustful. When risk controversies arise, a struggle may result between competing interests. The typical response from scientists and industry during risk controversies is that the public is irrational, the media twists information, and policy makers rarely listen to evidence. What begins as a scientific problem frequently turns into a social/public debate.

Science as a knowledge producer
Scientists speak from an authoritative position based on the ‘facts’, or knowledge claims, that they produce in their studies. However, this authoritative position depends on other scientists acknowledging the authority (or correctness of the ‘facts’) while denying voice to minority perspectives that may go against these knowledge claims. Philosopher Bruno Latour, who studied scientists and engineers at work, uses the analogy of the two-faced Janus to depict stages of science: ready-made science (where facts are agreed) and science in action (where facts are subject to debate). It is during this latter stage that contests occur between the credibility of both the producer of the knowledge claims and of the challenger. Although credibility contests may occur privately, it is when they move into the public domain that scientists may present a unified position to protect the autonomy of science.

Science to policy
Risks need to be measured by science before they gain social recognition. Claims then have to be translated within a policy domain, where only that knowledge that is made “meaningful” gains political attention. There is insufficient understanding of how environmental health policy decisions are made in the face of scientific uncertainty. Similarly, there is debate as to whether better science will lead to greater certainty in decision making. The tendency in science is to reduce phenomena to examinable components in the search for specific cause and effect relationships. This is not an easy task in environmental health where there is a multitude of effects. It becomes much more difficult to determine health effects if they are due to a combination of environmental assaults. Social considerations of what constitute important exposures and acceptable risk need to be incorporated into regulatory decisions.

One way to understand the interrelationships between various actors in the policy subsystem is through an examination of epistemic communities. Defined as a network of scientific experts with an authoritative claim to policy-relevant knowledge, an epistemic community provides consensual knowledge regarding issues of uncertainty, thereby facilitating policy intervention. Jasanoff and Wynne outline four defining characteristics of an epistemic community:

•shared normative and principled beliefs provid¬ing a value-based rationale for proposed social actions;
•shared causal beliefs;
•shared notions of validity including intersubjec¬tive, internally defined criteria for establishing valid knowledge; and
•a shared policy endeavour based on a commonly recognised set of problems.

Epistemic communities are often able to define problems in a particular light making some solutions more attractive to decision makers. Critiques of the concept argue that power in the policy arena is ignored. There is an assumption that it will only be expert knowledge that will sway decisions. Yet, policy decisions are usually made on the basis of a number of reasons with expert knowledge being only one input. Moreover, scientific facts are often not disputed; it is the interpretation of those facts that lead to uncertainty. Jamieson argues that science is viewed as a knowledge producer, and consequently, often it cannot bring public decisions to closure.

Agenda-setting analyses can assist in the examination of scientific discourses surrounding a public policy issue characterised by uncertainty. Kingdon outlines three factors that influence the agenda-setting process. The first is problem recognition. At any given moment, there is competition of ideas within a policy arena. Ideas that gain recognition move higher up the agenda to provide a focal point for different actors. Sometimes, problems are recognised by “focusing events”, which will draw attention to the issue through study and its uptake by a regulatory body. Second, in a regulatory arena, scientific analyses are requested to address policy problems. However, not all analyses are given the same attention, nor do they adequately address the problem as it is in the process of being defined by others. If the analysis addresses the problem, it will determine how long it remains relevant. New scientific knowledge may provide better understanding, thereby tipping the issue into the public/political consciousness. The political process itself is the third factor that may influence the agenda. The national mood or changes in the administration will influence the level of attention that an issue may receive on the agenda as well as funding.

Moving science to the public arena
Throughout processes of scientific uncertainty and the uptake of evidence in a policy domain, the role played by the public is often obscured. Largely, the public is viewed as innumerate and scientifically illiterate.

However, the public has a greater understanding of the role of science in understanding risk than scientists have of how public attitudes and beliefs are formed within a political participatory system. Tensions lie when the public, operating in a demosphere, do not fully trust uncertain scientific evidence arising from studies in the technosphere. It is not that the public are irrational, rather that they incorporate multiple considerations – social, cultural, political – beyond scientific evidence when making decisions about what is “risky”. This has led some to call for the coproduction of shared knowledge between scientists and lay audiences.

Summary
When existing scientific understandings are contested or different interpretations of the science are used within a policy arena, science can be used in multiple ways. Policymakers can choose to use scientific uncertainty as a means to justify a decision which runs counter to the evidence, or equally, uncertainty can be used to justify that no policy decision is needed. For the public, lay understandings of a risk situation, based on personal experiences, oral histories, or the media, may factor into their decision making process. There is a divide presently between quantitative risk assessments – which reflect a presentation of risk information – and public understanding of those risks; a divide that needs to be addressed through the development of good risk communication practice. Part of this process involves: knowledge translation of scientific evidence in plain language; sensitivity to the different sets of ‘knowledge’ (social, cultural, local) that the public uses to understand risk situations; and concerted efforts to work together as partners to develop shared understanding – both expert and lay.

This article is an edited version of
an entry in the “Encyclopedia of Quantitative Risk Analysis and
Assessment”, Copyright © 2008 John Wiley & Sons Ltd. Used by
permission.

www.wiley.com